Epidemiology of COVID-19 after Emergence of SARS-CoV-2 Gamma Variant, Brazilian Amazon, 2020–2021

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant has been hypothesized to cause more severe illness than previous variants, especially in children. Successive SARS-CoV-2 IgG serosurveys in the Brazilian Amazon showed that age-specific attack rates and proportions of symptomatic SARS-CoV-2 infections were similar before and after Gamma variant emergence.

The first study visit, between April and May 2018, targeted the 534 households drawn from the census listings; 1,391 residents from 354 households were located and agreed to participate. To achieve the desired sample size, 147 substitute households were randomly selected and approached during the second visit, in October-November 2018. The ongoing cohort is dynamic and new residents joining the household (those who moved in or were born between study visits) are enrolled during the follow-up visits. Study participants leaving the sampled households are retained in the cohort as long as they can be located by the field team and their new residences, which are labeled as new households, are situated in the urban area of Mâncio Lima. Six sequential house-to-house visits were carried out so far.
The median age of participants in the Mâncio Lima cohort study is 22 years (range, <1 to 103 years), with 51.3% of females. Among study participants ≥10 years of age, the literacy rate is 91.8%. Only 9.9% adult participants have a formal job. Most (80.0%) of study participants are supported by the Federal conditional cash transfer program called "Bolsa Família," a proxy of poverty.
Data and samples analyzed in the present study were obtained during serosurveys carried out in October-November 2020 and April-May 2021. The 2020 survey comprised 2,074 subjects distributed into 567 households, while the 2021 survey comprised 1,874 subjects distributed into 540 households. Overall, 1,408 individuals participated in both surveys; 1,215 (86.3%) of them (56.3% females) were tested for anti-SARS-CoV-2 antibodies on both occasions (main text, Figure 1). Compared to untested individuals, participants with antibody data in 2021 were significantly older (mean, 31.7 versus 26.4 years; p < 0.001, t-test), more likely to be females (56.3% versus 44.1%; p < 0.001, χ 2 test) and less likely to report at least one overnight stay outside Mâncio Lima within the past 6 months (29.1% versus 41.3%; p < 0.001, χ 2 test). No significant differences were observed regarding other covariates of interest.

SARS-CoV-2 IgG antibody detection
We tested 1,215 paired plasma samples, obtained from the same study participants at a 6month interval during the 2020 and 2021 surveys, for anti-SARS-CoV-2 IgG with a semiquantitative ELISA that uses the recombinant subdomain S1 of the Spike protein as antibody-capture antigen (EI 2606-9601 G; Euroimmun) (3). The assay has a sensitivity of 82.5% to 93.3% and specificity of 98.0% to 98.5% (4,5). Results from the 2020 survey were previously published (6).
We used a quantitative ELISA to investigate changes in specific antibody concentrations in selected paired plasma samples. To this end, we added to each microplate a standard curve with a serially diluted pool of 10 strongly positive plasmas (eight dilutions from 1:25 to 1:1:3,200). We defined that the pool had an antibody concentration of 100 arbitrary units (AU) at a 1:25 dilution. Antibody concentrations in test samples were interpolated using a four-parameter logistic regression model. Samples were tested at a 1:100 dilution and those with absorbance values outside the range of the standard curve (i.e., absorbances >3.363 or <0.527) were assigned antibody concentrations of 110 AU and 0.7 AU, respectively.

SARS-Cov-2 detection
To characterize SARS-CoV-2 lineages circulating during the first and second waves, we obtained two nasopharyngeal swab samples from 49 consecutive symptomatic patients (age range, 3-77 years) seeking COVID-19 testing in Mâncio Lima in August 2020 and again from 49 patients (age range, 4-86 years) in April 2021. Results obtained with the samples collected in 2020 were published elsewhere (6).
One swab collected between 21 and 29 April 2021 was used for point-of-care antigenbased diagnosis (ECO F COVID-19 Ag test FA0054; Ecodiagnostica, Corinto, Brazil) and the other was preserved in RNA/DNA Shield (Zymo Research, Irvine, CA) for RNA extraction.
Template RNA was prepared using QIAamp Viral RNA mini kits (Qiagen, Hilden, Germany).
We tested antigen-positive samples for SARS-CoV-2 RNA by reverse transcription PCR by using the China CDC protocol that targets the ORF1ab and N genes (XGEN Master COVID-19 kit, Mobius Life Science, Pinhais, Brazil). Target amplification was carried out as described (7).

SARS-CoV-2 genome sequencing
We selected 15 samples with cycle threshold <30 for whole-genome sequencing as part of a countrywide SARS-CoV-2 genomic surveillance project (8). Template RNA was converted to cDNA using the Protoscript II First Strand cDNA synthesis Kit (New England Biolabs, Cambridge, MA) and random hexamers. Whole-genome amplification was performed with multiplex PCR amplification using the SARS-CoV-2 primer scheme (V1 to V3) and Q5 High-Fidelity DNA polymerase (New England Biolabs, UK), by using ARTIC protocol (https://www.protocols.io/view/ncov-2019-sequencing-protocol-bbmuik6w?version_warning = no). PCR products were cleaned-up using AmpureXP purification beads (Beckman Coulter, High Wycombe, UK) and quantified using the Qubit dsDNA High Sensitivity assay on the Qubit 3.0 instrument (Life Technologies, Thermo Fischer Scientific, USA). Amplicons from each sample were normalized and pooled in an equimolar fashion and barcoded using the EXP-NBD104 (1-12) and EXP-NBD114 (13-24) Native Barcoding Kits (Oxford Nanopore Technologies, UK). Concentrations of double-stranded DNA for the librarynegative controls were below detection levels, indicating no contamination.
Nanopore sequencing on the MinION platform (Oxford Nanopore, Oxford, UK) was carried out libraries were generated using the SQK-LSK109 Kit (Oxford Nanopore) and were loaded onto an R9.4.1 flow-cell (Oxford Nanopore). RAMPART software from the ARTIC Network (https://artic.network/ncov-2019/ncov2019-using-rampart.html) was used to monitor the sequencing run in real time to estimate the coverage depth (target, 200×). With the Guppy software version 4.4.0 (Oxford Nanopore Technologies), fastq files were base-called, demultiplexed, and trimmed. Sequencing data were subjected to sequence quality controls and the consensus genomes were obtained by the mapping of fastq files to Wuhan-Hu 1 reference genome (GenBank Accession Number MN908947).
Assembled sequences of 11 isolates (out of 15) yielded at least 50% coverage of the SARS-CoV-2 genome, with at least 20× depth. Lineages were classified using the Pangolin COVID-19 Lineage Assigner software tool (http://pangolin.cog-uk.io/) and phylogenetic analysis using complete reference genomes. Sequencing statistics and lineage assignment information are provided in Appendix Table 1.

Estimating COVID-19 attack rates
We used IgG positivity during the first survey (October-November 2020) as a proxy of SARS-CoV-2 infection during the first wave. The crude antibody prevalence (%) in October-November 2020, a proxy of the COVID-19 attack rate between April and November 2020, was calculated as number of IgG positive persons (n = 407) divided by the number of participants tested (n = 1215) × 100, with exact binomial 95% confidence intervals. We used IgG seroconversion detected in the second survey as a proxy of SARS-CoV-2 infection during the second wave. The attack rate between surveys was calculated as the number of IgG seroconversions in April-May 2021 in participants who had not been vaccinated (n = 209) divided by the number of participants who were IgG-negative during the first survey (n = 729).
To this end, we excluded from both the numerator and the denominator the 79 participants who As well as crude antibody prevalence, we also present sensitivity and specificity adjusted prevalence estimates. We used a Bayesian framework that propagates uncertainty in the sensitivity and specificity estimates of the test (9). We used the validation data from Naaber et al.  Table 2); (ii) clinically apparent COVID-19 during the first wave (Appendix Table 3), (iii) clinically apparent COVID-19 upon serologically documented SARS-CoV-2 infection during the first wave (Appendix Table 4); (iv) SARS-CoV-2 infection (using IgG seroconversion as a proxy) during the second wave, among participants who were seronegative during the first survey (October-November 2020; Appendix Table 2); (v) clinically apparent COVID-19 during the second wave among participants who were seronegative during the first survey (October-November 2020; Appendix Table 3); and (vi) clinically apparent COVID-19 during the second wave among participants who were seronegative during the first survey (October-November 2020) and seroconverted by April-May 2021 (Appendix Table 4).
Note that models (ii) and (iii), as well as models (v)  Because study participants are nested into households, which introduces dependency among observations, for each outcome we built mixed-effects Poisson regression models with random effects at the household level and robust variance. Individual covariates were age in October-November 2020 (categorical variable), sex (female versus male), laboratory-confirmed malaria within the past 12 months (no versus yes), overnight stay(s) away from Mâncio Lima within the past 12 months (no versus yes), and DENV seropositivity in the previous serosurvey (either October-November 2019 or October-November 2020; no versus yes). Household covariates were wealth index quintiles (6) and household size. Age, sex, and covariates associated with the outcome at a significance level <20% in unadjusted analysis were retained in multiple Poisson regression models. Participants with missing values were excluded from the adjusted models. Statistical significance was defined at the 5% level; relative risk (RR) estimates are provided along with 95% confidence intervals (CIs) to quantify the influence of each predictor on the outcome, while controlling for all other covariates (11).

SARS-CoV-2 attack rates during the first and second waves
We observed a higher attack rate between April and November 2020 (33.5%; 95% CI, 30.8%-36.2%) compared with that between November 2020 and April 2021 (28.7%; 95% CI, 25.4%-32.1%). However, differences in attack rate over time must not be overinterpreted because populations at risk are not entirely comparable during the first and second waves. We argue that SARS-CoV-2 has affected disproportionately the most exposed and most susceptible persons in our heterogeneous cohort population. High-risk participants were infected first and developed specific antibodies more rapidly; as a consequence, SARS-CoV-2 transmission during the first epidemic wave may have selectively removed high-risk individuals from the pool of seronegatives (12). Moreover, some high-risk population strata (health professionals and persons >60 years of age) were selectively vaccinated (see below). A proportionally larger fraction of individuals who remained seronegative after the first wave is expected to be either unexposed or little susceptible to SARS-CoV-2 infection, limiting virus spread during the second wave. This concept is illustrated in Appendix Figure 1.

Predictors of SARS-CoV-2 infection and clinically apparent COVID-19 during the first and second epidemic waves
Participants living in crowded households (≥7 people) were at increased risk of SARS-CoV-2 infection during both the first and second waves. Female sex and affluence (highest wealth index quintile) were significantly associated with an increased risk of infection only during the first wave, while age ≥50 years predicted a decreased risk of infection only during the second wave (Appendix Table 2).
We considered the following self-reported symptoms to define clinically apparent COVID-19: new or increased fever, cough, shortness of breath, chills, muscle pain, loss of taste or smell, sore throat, diarrhea, or vomiting within the past 6 months. Children ≤5 years of age tended to be at lower risk of clinically apparent COVID-19 than adults during both waves, although statistical significance was not reached in most comparisons (Appendix Table 3). In addition, affluence and household crowding were associated with a significantly increased risk of clinically apparent COVID-19 during the first wave (Appendix Table 3). The previously described association between a positive DENV IgG serology and subsequent risk of clinically apparent COVID-19 (6) reached statistical significance only during the first wave.
We next used multiple Poisson regression models to identify the predictors of clinically apparent COVID-19 among study participants with serologically proven SARS-CoV-2 infection during the first wave (IgG seropositivity in October-November 2020; n = 359 after excluding persons with missing information) and the second wave (IgG seroconversion in April-May 2021; n = 209). We further confirm that, during both waves, study participants >15 years of age tended to be similarly more likely to develop symptoms, once infected with SARS-COV-2, than young children (Appendix Table 4).
Some of the symptoms used to define clinically apparent COVID-19 may be found in other locally prevalent infectious diseases, such as malaria, dengue and common upper your upper respiratory tract. Malaria is unlikely to be a confounder in this population (Appendix Tables 2 and 4; see also reference 6), but the annual dengue transmission season (November to April) overlapped with the second SARS-CoV-2 wave in 2020-21. As a consequence, the proportion of symptomatic SARS-CoV-2 infections during the second wave may have been slightly overestimated due to dengue symptoms reported by our study participants.

COVID-19 severity during the first and second waves
We found no evidence that SARS-CoV-2 infections acquired during the second epidemic wave, dominated by the Gamma variant, are more likely to be symptomatic in our study population. In contrast, a recent study has shown that, among people hospitalized in Brazil due to COVID-19, the median age of patients decreased (63 years vs 59 years), with a relative increase of 18% in the proportion of patients younger than 60 years during the second wave (the period from week 44 in 2020 to week 21 in 2021) compared with the first wave (weeks 8 to 43 in 2020).
There are several factors that may have contributed to these results. First, we can hypothesize that individuals at increased risk of infection may have been preferentially infected during the first wave. In addition, individuals >60 years were among the early targets of mass vaccination campaigns. As individuals who have been vaccinated or experienced natural infection are less likely to develop severe disease once (re)infected during the second wave, some differences in age-specific hospitalization rates are expected. In other works, individuals at high risk (including those vaccinated in early 2021) were selectively removed from the "susceptible pool" (Appendix Figure 1).
Second, individuals admitted to overwhelmed hospitals during the second epidemic wave, which was particularly intense in Brazil, are likely to have, on average, a more severe disease than those admitted during the first wave. The number of hospital admissions mirrors the number of available beds, not necessarily the number of patients who required intensive care.
Patients with more threatening clinical conditions are expected to be selectively admitted when few hospital beds are available. Abbreviations: AIC, Akaike information criterion; CI, confidence interval; and RR, relative risk. "Past dengue" refers to dengue fever seropositivity in the previous survey (2019 serology for 2020 models and 2020 serology for 2021 models). *Totals may vary for some covariates due to missing data. †The adjusted model corresponds to the following STATA syntax: mepoisson outcome indevars housevars || household: vce(robust) irr. Relative risks are calculated for individual (indevars) and householdlevel covariates (housevars) included in the fixed-effects component.